In Ghana Demographic Health Survey (GDHS), information is collected on the demographic characteristics and health status which is representative sample of the entire population. The backbone for the survey is enumeration areas (EA), clusters which was done using two-stage probabilistic approach. This paper illustrates analysis of childhood mortality by adjusting for cluster effect using Generalized Estimation Equations (GEE). Ghana Demographic Survey Data -2008 (GDHS-2008) was used for the analysis. GEE model with three working correlation matrices independence, unstructured and exchangeable were adjusted for the data set. Logistic regression models and statistical tools were used to find association and select significant variables on childhood mortality. Age of mother, Total birth in last five years and region of residence were significance determinants of incidence of childhood mortality. We recommend that there should be clear policy and programs for educating, campaigning and increasing and improving health facilities. Suggestions for further study of childhood mortality were also in this paper.
| Published in | International Journal of Statistical Distributions and Applications (Volume 3, Issue 4) |
| DOI | 10.11648/j.ijsd.20170304.16 |
| Page(s) | 95-102 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
GEE, Childhood Mortality, Cluster, Ghana Demographic Health Survey (GDHS)
| [1] | Abuqumar, M., Danny, C., & Fred, L. (2010). Impact of parental education on infant mortality in Gaza Strip,. Full Length Research Paper. |
| [2] | Akoto, & Tambashe. (2000, January 17). S0cioeconomic inequalities of infant and child mortality among rural and urban areas in sub-saharan. Retrieved 2016, from.:. http://www.demogr.mpg.de/Papers/workshops/020619_paper01.pd. |
| [3] | Balk, P., & Neuman, S. G. (2003). Analysis of Childhood mortality in West Africa Based on their DGHS data. African Indepth Journal. |
| [4] | Becares, L., Cormack, D., & Harris, R. (2013). Ethnic density and area deprivation: Neighbourhood effects on Maori health and racial discrimination in Aotearoa/New Zealand. Soc Sci Med,. Social Science & Medicine, 88 (PMCID: PMC3725420), 76–82. |
| [5] | Becher, H. (2010). Analysis of Mortality Clustering at member HDSSs within the INDEPTH Network-an important public health issue. Global Health action Supplement, 3(Sup 1) (PMCID: PMC2935924), 3-7. |
| [6] | Das Gupta, M. (1990). Death Clustering Mohers Education and Child Mortality. 489-505: Population Studies. |
| [7] | Espo, M. (2002). Chiild Mortality in Malaw from environmental factors. Health Transition Review. |
| [8] | GDHS, G. D. (2008). Demographic Health Survey. Accra: Ghana Statistical Service. |
| [9] | Heisler, E. J. (2012). Disparities in child mortality, racial factor. Congressional Research Service, 8 (R41378), 1-29. |
| [10] | Hill, K., & Mahy, M. (2001). childhood mortality in Kenya:Examination of trend and determinants inthe late 1980s andmid 1990s. USA: John Hopkins University/macro Internayional Inc. |
| [11] | Hobcraft, J. (1992). Fertility Pattern and Child survival, Comperative analysis. Population Bulletin of the United nation, 1-31. |
| [12] | Hogan et al. (2009). Muiltivariate analysis. |
| [13] | Hosseinpoor. (2005). Socioeconomic inequality in infant mortality in Iran and across Its Province. Technical Report, Bullitin of WHO. |
| [14] | Jacoby, H., & Wang, L. (2003). Infant mortality in Zambia Socioeconomic and demographic correlates. Washington DC: World Bank. |
| [15] | Johanna, S. (2016). Determinant of Under 5 in Uganda. Masters Thesis, University of Gottenburg, Uganda, 1-30. |
| [16] | Kanmiki, E. W., & Bawah, A. A. (2014). Socio- economic and demographic determinant of under-five mortality in rural northern Ghana. BioMed Central. |
| [17] | McCullagh, P., & Nelder, J. (1989). generlised linear model. Chapman and Hall, New York. |
| [18] | Mosley, W. H., & Chen, L. C. (1984). An Analytical framework for the study of child survival in developng countries. Population and Development Review. |
| [19] | Mutunga, C. J. (2004). Environmental Determinant of Child mortality. Kenya Institute of Public policy. |
| [20] | Obed, E. A., Owusu, A., & Nartey, C. Z. (2010). Custering childhood mortality in Kintampo. Global Health Suppliment action. |
| [21] | Ogada, O. (2014). SOCIOECONOMIC DETERMINANTS OF UNDER-FIVE MORTALITY IN SOCIOECONOMIC DETERMINANTS OF UNDER-FIVE MORTALITY IN. Unpublished thesis, University of Nairobi. |
| [22] | Rafiqul, I., Moazzem, H., Mizanur, R., & Mosharaf, H. (2013). Impact of Socio-demographic Factors on child Mortality in Bangladesh: An Multivariate Approach. International Journal of Psychology and Behavioral Science, 34-39. |
| [23] | Sastry, N. (1997). Community Characteristics, individual and household attributing to under-five mortality. Working Paper series, 13-96. |
| [24] | Singh, R., & Tripathi, V. (2013). Maternall factor contributing to under- five mortality at birth order 1 to 5 in india. Spingplu. |
| [25] | UNICEF. (2009). Technical Report:State of Childern. New York, NY 10017, USA: 3 United Nations Plaza. |
| [26] | UNICEF. (2010). Tracking Progress on Child and Maternal Nutrition. New York, NY 10017, USA: United Nations Children’s Fund. |
| [27] | Wang, L. (2003). Determinant of Child Mortality: Empirical Finding from demographic and health Survey. |
| [28] | WHO, W. H. (2000). Technical Report. Geneva: World Health Organis. |
| [29] | Woldemicael, G. (2000). The effect of water supply and sanitation on Chiid mortality in Urban areas. Journal of Biosocial Science, 32, 207-227. |
| [30] | Yeboah, D. (2014). Classification tree as a tool for decomposing inequalities in under-five mortality in Ghana. Master Thesis, KNUST. |
| [31] | Zeger, S., & Liang, Y. (1986). Longitudinal data Analysis Using Generalised Model. Biometrika, 13-22. |
APA Style
Kankam Stephen, Nana Kena Frimpong, Kofi Adagbodzo Samuel. (2017). Childhood Mortality Adjusting for Cluster Effect Study in Ghana Demographic Health Survey. International Journal of Statistical Distributions and Applications, 3(4), 95-102. https://doi.org/10.11648/j.ijsd.20170304.16
ACS Style
Kankam Stephen; Nana Kena Frimpong; Kofi Adagbodzo Samuel. Childhood Mortality Adjusting for Cluster Effect Study in Ghana Demographic Health Survey. Int. J. Stat. Distrib. Appl. 2017, 3(4), 95-102. doi: 10.11648/j.ijsd.20170304.16
AMA Style
Kankam Stephen, Nana Kena Frimpong, Kofi Adagbodzo Samuel. Childhood Mortality Adjusting for Cluster Effect Study in Ghana Demographic Health Survey. Int J Stat Distrib Appl. 2017;3(4):95-102. doi: 10.11648/j.ijsd.20170304.16
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijsd.20170304.16,
author = {Kankam Stephen and Nana Kena Frimpong and Kofi Adagbodzo Samuel},
title = {Childhood Mortality Adjusting for Cluster Effect Study in Ghana Demographic Health Survey},
journal = {International Journal of Statistical Distributions and Applications},
volume = {3},
number = {4},
pages = {95-102},
doi = {10.11648/j.ijsd.20170304.16},
url = {https://doi.org/10.11648/j.ijsd.20170304.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20170304.16},
abstract = {In Ghana Demographic Health Survey (GDHS), information is collected on the demographic characteristics and health status which is representative sample of the entire population. The backbone for the survey is enumeration areas (EA), clusters which was done using two-stage probabilistic approach. This paper illustrates analysis of childhood mortality by adjusting for cluster effect using Generalized Estimation Equations (GEE). Ghana Demographic Survey Data -2008 (GDHS-2008) was used for the analysis. GEE model with three working correlation matrices independence, unstructured and exchangeable were adjusted for the data set. Logistic regression models and statistical tools were used to find association and select significant variables on childhood mortality. Age of mother, Total birth in last five years and region of residence were significance determinants of incidence of childhood mortality. We recommend that there should be clear policy and programs for educating, campaigning and increasing and improving health facilities. Suggestions for further study of childhood mortality were also in this paper.},
year = {2017}
}
TY - JOUR T1 - Childhood Mortality Adjusting for Cluster Effect Study in Ghana Demographic Health Survey AU - Kankam Stephen AU - Nana Kena Frimpong AU - Kofi Adagbodzo Samuel Y1 - 2017/11/28 PY - 2017 N1 - https://doi.org/10.11648/j.ijsd.20170304.16 DO - 10.11648/j.ijsd.20170304.16 T2 - International Journal of Statistical Distributions and Applications JF - International Journal of Statistical Distributions and Applications JO - International Journal of Statistical Distributions and Applications SP - 95 EP - 102 PB - Science Publishing Group SN - 2472-3509 UR - https://doi.org/10.11648/j.ijsd.20170304.16 AB - In Ghana Demographic Health Survey (GDHS), information is collected on the demographic characteristics and health status which is representative sample of the entire population. The backbone for the survey is enumeration areas (EA), clusters which was done using two-stage probabilistic approach. This paper illustrates analysis of childhood mortality by adjusting for cluster effect using Generalized Estimation Equations (GEE). Ghana Demographic Survey Data -2008 (GDHS-2008) was used for the analysis. GEE model with three working correlation matrices independence, unstructured and exchangeable were adjusted for the data set. Logistic regression models and statistical tools were used to find association and select significant variables on childhood mortality. Age of mother, Total birth in last five years and region of residence were significance determinants of incidence of childhood mortality. We recommend that there should be clear policy and programs for educating, campaigning and increasing and improving health facilities. Suggestions for further study of childhood mortality were also in this paper. VL - 3 IS - 4 ER -
Information
Email: